CAREER: Plenoptic Scene Reconstruction

职业：全光场景重建

• 批准号: 9984672
• 负责人: Steven Seitz
• 金额: $ 9.78万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-05-15 至 2000-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984672&HistoricalAwards=false
• 关键词: CAREER; Plenoptic; Scene; Reconstruction

This is the first year funding of a four year continuing award. The objective of this project is to develop a research and educational program for modeling complex real?world environments from photographs. We are motivated by the following questions: suppose one could photograph a scene from every possible viewing position and orientation. What could be inferred about the structure of the scene and via which algorithms? To answer these questions, we propose a formal study of the scene reconstruction problem from the standpoint of the plenopticfunction, a 5D function that encodes the space of all possible images of a scene.The project will focus on two primary issues: (1) acquiring plenoptic representations of real scenes, and (2) reconstructing scene geo metry and radiance from such representations. To address the first problem, we propose a principled approach to the problem of image acquisition, i.e., along which directions should scene radiance be sampled in order to obtain the best possible scene reconstructions. Based on this analysis, we propose novel plenoptic cameras that are optimized for reconstruction tasks. The second problem is to devise algorithms for computing scene geometry and radiance from plenoptic representations. In contrast to traditional approaches which are based on perspective images, the proposed algorithms will operate directly on the plenoptic function and integrate information from a continuum of viewpoints. This formulation introduces unique challenges due to correspondence, visibility, and scale that will be addressed in the proposed work.A key outcome of the proposed work will be practical techniques for constructing 3D models of complex realworld environments. This capability is central for a variety of robotics tasks such as visual servoing, robot navigation, and motion planning. Environment modeling will also facilitate numerous applications in computer?aided design and computer graphics, including visualization of remote objects and environments over the Internet, virtual studios for television and film, and 3D virtual teleconferencing.An integral part of the project is a long term educational program for scene modeling in computer vision and graphics, emphasizing curriculum development, research opportunity for students, and outreach activities. An important component will be to integrate concepts from vision, graphics, and image processing within the undergraduate curriculum at CMU, and to design new interdisciplinary courses on environment capture and synthesis. The involvement of both undergraduate and graduate students is integral to the proposed research plan, and students will be encouraged to contributed to ongoing research activities. Outreach activities will include organizing tutorials and courses to disseminate research results to the larger community.

这是一个为期四年的连续奖励的第一年资助。 这个项目的目标是开发一个研究和教育计划，为复杂的真实的？从照片中看到的世界环境。我们的动机是以下问题：假设一个人可以从每一个可能的观察位置和方向拍摄一个场景。关于场景的结构，可以通过哪些算法推断出什么？为了回答这些问题，我们提出了一个正式的场景重建问题的研究从全光函数的角度来看，一个5D函数，编码的空间的所有可能的图像的scene.The项目将集中在两个主要问题：（1）获取全光表示的真实的场景，（2）重建场景的几何和辐射从这样的表示。为了解决第一个问题，我们提出了一个原则性的方法来解决图像采集的问题，即，应沿着哪些方向对场景辐射进行采样，以便获得最佳的场景重建。基于这种分析，我们提出了新的全光相机，重建任务进行了优化。第二个问题是设计用于从全光表示计算场景几何形状和辐射的算法。与基于透视图像的传统方法相比，所提出的算法将直接对全光函数进行操作，并从连续的视点整合信息。该公式引入了独特的挑战，由于对应性，可见性和规模，将在拟议的work.A拟议的工作的一个关键成果将是实用的技术，构建复杂的现实世界环境的三维模型。这种能力是各种机器人任务的核心，如视觉伺服，机器人导航和运动规划。环境建模也将促进许多应用程序在计算机？辅助设计和计算机图形学，包括在互联网上的远程对象和环境的可视化，电视和电影的虚拟演播室，以及3D虚拟电话会议。该项目的一个组成部分是计算机视觉和图形学场景建模的长期教育计划，强调课程开发，学生的研究机会和推广活动。一个重要的组成部分将是在CMU的本科课程中整合视觉，图形和图像处理的概念，并设计新的跨学科课程环境捕捉和合成。本科生和研究生的参与是拟议的研究计划的组成部分，并鼓励学生为正在进行的研究活动做出贡献。外联活动将包括组织辅导课和课程，向广大社区传播研究成果。